Users with information needs often construct search requests that reflect these needs for submission to a search engine. For example, a user might construct and submit a query in Structured Query Language (SQL) format to obtain availability, pricing, and other information concerning a part used in manufacturing a product. These queries typically limit a user to Boolean operators in expressing needs, which may prevent the user from fully and precisely expressing the needs and thus prevent the search engine from retrieving results that optimally satisfy the needs. As an example, the user might query a parts catalog for all available capacitors with capacitances between C1 and C2, voltage characteristics between V1 and V2, and temperature characteristics between T1 and T2. In response to the query, the search engine returns search results to the user identifying all the capacitors in the parts catalog that satisfy the query.
Although the search engine may very easily return all the capacitors within the “hypercube” defined by these capacitance, voltage, and temperature parameters, the user may still be forced to manually evaluate the information to sort the capacitors according to their overall suitability in order to select a particular capacitor. Where the catalog of parts is relatively large, such a query may yield a huge number of results, making the manual evaluation and sorting of these results a daunting task. It is often very difficult for the user to assess various tradeoffs associated with the results, none of which may match all of the needs exactly. Where the number of parameters (“dimensionality”) of the query is also large, this task may become even more difficult. Moreover, if queries for a large number of items are processed on a continuing basis, such as in connection with parts procurement for a large manufacturing organization, this task may become truly mind-boggling. Analogous deficiencies may arise in the context of negotiations between parties with respect to items.